Policy capturing is a decision analysis technique normally using linear statistical models to infer the basis of expert judgments. The purpose of the present work was to test if the C4.5 decision tree (DT) algorithm (a non-linear machine learning method) is more effective at capturing individual’s decision policies than the standard linear technique. Human classification behavior was measured i...

Background: Early outcome prediction of hospitalized patients is critical because the intensivists are constantly striving to improve patients' survival by taking effective medical decisions about ill patients in Intensive Care Units (ICUs). Despite rapid progress in medical treatments and intensive care technology, the analysis of outcomes, including mortality prediction, has been a challenge ...

Data mining (DM) techniques are being increasingly used in many modern organizations to retrieve valuable knowledge structures from organizational databases, including data warehouses. An important knowledge structure that can result from data mining activities is the decision tree (DT) that is used for the classi3cation of future events. The induction of the decision tree is done using a super...

Intrusion detection system (IDS) is to monitor the attacks occurring in the computer or networks. Anomaly intrusion detection plays an important role in IDS to detect new attacks by detecting any deviation from the normal profile. In this paper, an intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection is proposed. The key idea is to take the adva...

Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty. Bayesian averaging also allows experts to estimate the uncertainty accurately when a ...

Predicting the quality of software modules prior to testing or system operations allows a focused software quality improvement endeavor. Decision trees are very attractive for classification problems, because of their comprehensibility and white box modeling features. However, optimizing the classification accuracy and the tree size is a difficult problem, and to our knowledge very few studies ...

The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models used within such a technique gives exp...

Wireless sensor networks (WSNs) connect physical sensors that are distributed in the environment. In many applications, the statistical pattern recognition methods, such as decision tree (DT) algorithms, are used to recognize the patterns of the sensor readings. To enable the decision tree classification (DTC) in WSNs, a new distributed decision tree classification (DDTC) algorithm based on mob...

Introduction. The automatic construction of classifiers is an important research problem in data mining, since it provides not only a good prediction but provides also a characterization of a given data in the form easily understood by a human. A decision tree [4] is a classifier widely used in real applications, which are easy to understand, and efficiently constructed by using a method based ...

In wind energy studies, predicting the short-term generation amount for power plants and determining production offer to be placed on market play an important role. this study hourly estimation of a turbine located in Turkey with installed 3600 kW has been made. Estimation results were evaluated seasonal annual basis. New hybrid models have developed prediction, consisting Bayesian Optimization...